Control of Air Free-Cooled Data Centers in Tropics via Deep Reinforcement Learning

Abstract

Air free-cooled data centers (DCs) have not existed in the tropical zone due to the unique challenges of year-round high ambient temperature and relative humidity (RH). The increasing availability of servers that can tolerate higher temperatures and RH due to the regulatory bodies' prompts to raise DC temperature setpoints sheds light upon the feasibility of air free-cooled DCs in tropics. This paper studies the problem of controlling the temperature and RH of the air supplied to the servers in a free-cooled tropical DC below certain thresholds to maintain servers' computing performance and reliability. To achieve the goal, a portion of the hot air generated by the servers is recirculated and mixed with the fresh outside air to adjust the RH of the supply air. To address the complex psychrometric dynamics, we apply deep reinforcement learning to learn the control policy that aims at minimizing the energy used for moving air and on-demand cooling. Extensive evaluation based on real data traces collected from an air free-cooled testbed and comparisons with hysteresis-based and model-predictive control approaches show the superior performance of our solution.